A novel target item-based similarity function in privacy-preserving collaborative filtering

Bilge, Alper

A novel target item-based similarity function in privacy-preserving collaborative filtering

dc.authorid	0000-0003-3467-9915
dc.contributor.author	Bilge, Alper
dc.date.accessioned	2024-11-19T08:28:24Z
dc.date.available	2024-11-19T08:28:24Z
dc.date.issued	27.05.2024
dc.department	Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstract	Memory-based collaborative filtering schemes are among the most effective recommendation technologies in terms of prediction quality, despite commonly facing issues related to accuracy, scalability, and privacy. A prominent approach suggests an intuitively reasonable modification to the similarity function, which has been proven to provide more accurate recommendations than those generated by state-of-the-art memory-based collaborative filtering methods. However, this scheme exacerbates the scalability problem due to additional computational costs and fails to protect individual privacy. In this study, we recommend using a preprocessing method to eliminate relatively dissimilar items from the prediction estimation process, thereby enhancing the scalability of the proposed approach. We explore how to provide recommendations based on the previously proposed similarity function while preserving privacy and propose privacy-preserving schemes to accomplish this task. Additionally, we apply our preprocessing approach to our proposed privacy-preserving schemes to improve both scalability and accuracy. After analyzing our schemes with respect to privacy and additional costs, we conduct experiments with real data to examine the impact of our schemes on scalability and accuracy. The empirical outcomes indicate that our preprocessing scheme significantly alleviates scalability issues in both conventional and privacy-preserving environments and enhances accuracy within privacy-preserving frameworks.
dc.identifier.citation	Yalcin, E., Bilge, A. A novel target item-based similarity function in privacy-preserving collaborative filtering. J Supercomput 80, 19433–19461 (2024). https://doi.org/10.1007/s11227-024-06221-7
dc.identifier.doi	10.1007/s11227-024-06221-7
dc.identifier.endpage	19461
dc.identifier.scopusquality	Q1
dc.identifier.startpage	19433
dc.identifier.uri	https://hdl.handle.net/20.500.12418/30877
dc.identifier.volume	80
dc.identifier.wosquality	Q2
dc.indekslendigikaynak	Web of Science
dc.institutionauthor	Yalcin, Emre
dc.institutionauthorid	0000-0003-3818-6712
dc.language.iso	en
dc.publisher	Springer
dc.relation.ispartof	The Journal of Supercomputing
dc.relation.publicationcategory	Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Privacy ·
dc.subject	Accuracy
dc.subject	Scalability
dc.subject	Collaborative fltering
dc.subject	Preprocessing
dc.subject	Prediction
dc.title	A novel target item-based similarity function in privacy-preserving collaborative filtering
dc.type	Article